update data implementation

alphamind/data/dbmodel/models.py

@@ -858,6 +858,7 @@ class Models(Base):
     model_version = Column(BigInteger, nullable=False)
     update_time = Column(DateTime, nullable=False)
     model_desc = Column(JSONB, nullable=False)
+    data_meta = Column(JSONB, nullable=True)
     is_primary = Column(Boolean)
     model_id = Column(Integer, primary_key=True, autoincrement=True)
 

alphamind/model/linearmodel.py

@@ -57,7 +57,7 @@ class ConstLinearModel(ModelBase):
 
 class LinearRegression(ModelBase):
 
-    def __init__(self, features: list = None, fit_intercept: bool = False, **kwargs):
+    def __init__(self, features=None, fit_intercept: bool = False, **kwargs):
         super().__init__(features)
         self.impl = LinearRegressionImpl(fit_intercept=fit_intercept, **kwargs)
 
@@ -84,7 +84,7 @@ class LinearRegression(ModelBase):
 
 class LassoRegression(ModelBase):
 
-    def __init__(self, alpha=0.01, features: list = None, fit_intercept: bool = False, **kwargs):
+    def __init__(self, alpha=0.01, features=None, fit_intercept: bool = False, **kwargs):
         super().__init__(features)
         self.impl = Lasso(alpha=alpha, fit_intercept=fit_intercept, **kwargs)
 
@@ -111,7 +111,7 @@ class LassoRegression(ModelBase):
 
 class LogisticRegression(ModelBase):
 
-    def __init__(self, features: list = None, fit_intercept: bool = False, **kwargs):
+    def __init__(self, features=None, fit_intercept: bool = False, **kwargs):
         super().__init__(features)
         self.impl = LogisticRegressionImpl(fit_intercept=fit_intercept, **kwargs)
 

alphamind/model/treemodel.py

@@ -5,7 +5,6 @@ Created on 2017-12-4
 @author: cheng.li
 """
 
-from typing import List
 from distutils.version import LooseVersion
 import arrow
 import numpy as np
@@ -27,7 +26,7 @@ class RandomForestRegressor(ModelBase):
     def __init__(self,
                  n_estimators: int=100,
                  max_features: str='auto',
-                 features: List=None,
+                 features=None,
                  **kwargs):
         super().__init__(features)
         self.impl = RandomForestRegressorImpl(n_estimators=n_estimators,
@@ -59,7 +58,7 @@ class RandomForestClassifier(ModelBase):
     def __init__(self,
                  n_estimators: int=100,
                  max_features: str='auto',
-                 features: List = None,
+                 features=None,
                  **kwargs):
         super().__init__(features)
         self.impl = RandomForestClassifierImpl(n_estimators=n_estimators,
@@ -92,7 +91,7 @@ class XGBRegressor(ModelBase):
                  n_estimators: int=100,
                  learning_rate: float=0.1,
                  max_depth: int=3,
-                 features: List=None,
+                 features=None,
                  n_jobs: int=1,
                  **kwargs):
         super().__init__(features)
@@ -171,7 +170,7 @@ class XGBTrainer(ModelBase):
                  early_stopping_rounds=None,
                  subsample=1.,
                  colsample_bytree=1.,
-                 features: List = None,
+                 feature=None,
                  random_state: int=0,
                  n_jobs: int=1,
                  **kwargs):